Presentation delivered at Lehigh University (Bethlehem, PA) on Friday, April 26, 2019.
This presentation begins with discussing the history of the cheminformatics field. In addition, it also discusses a question "what makes cheminformatics different from bioinformatics?" (by comparing the ways in which molecules are described and compared in the two fields).
Presentation delivered at Lehigh University (Bethlehem, PA) on Friday, April 26, 2019.
This presentation begins with discussing the history of the cheminformatics field. In addition, it also discusses a question "what makes cheminformatics different from bioinformatics?" (by comparing the ways in which molecules are described and compared in the two fields).
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
Chemical risk assessment is often limited by the lack of experimental toxicity data for a large number of diverse chemicals. In the absence of experimental data, potential chemical hazard is often predicted using data gap filling techniques such as quantitative structure activity relationship (QSAR) models. QSARs are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR tools are a widely utilized alternative to time-consuming clinical and animal testing methods, yet concerns over reliability and uncertainty limit application of QSAR models for regulatory chemical risk assessments. The reliability of a QSAR model depends on the quality and quantity of experimental training data and the applicability domain of the model. This talk will describe the basics concepts and best practices in QSAR modeling, principles associated with validation of QSAR models, summary of available QSAR tools, limitations and challenges in the acceptance of QSAR models, and the current status and prospects of QSAR modeling methods in the medical devices community.
Computer-aided drug design (CADD) is a widely used technology using computational tools and resources for the storage, management, analysis and modeling of compounds. It relies on digital repositories for study of designing compounds with physicochemical characteristics, predicting whether a given molecule will be combined with the target, and if so how strongly. Computer based methods can help us to search new hits in drug discovery, screen many irrelevant compounds at the same time and study the structure-activity relationship of drug molecules.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
Molecular Dynamics for Beginners : Detailed OverviewGirinath Pillai
Detailed presentation of what is molecular dynamics, how it is performed, why it is performed, applications, limitations and software resources on how to perform calculations are discussed.
Chemical risk assessment is often limited by the lack of experimental toxicity data for a large number of diverse chemicals. In the absence of experimental data, potential chemical hazard is often predicted using data gap filling techniques such as quantitative structure activity relationship (QSAR) models. QSARs are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR tools are a widely utilized alternative to time-consuming clinical and animal testing methods, yet concerns over reliability and uncertainty limit application of QSAR models for regulatory chemical risk assessments. The reliability of a QSAR model depends on the quality and quantity of experimental training data and the applicability domain of the model. This talk will describe the basics concepts and best practices in QSAR modeling, principles associated with validation of QSAR models, summary of available QSAR tools, limitations and challenges in the acceptance of QSAR models, and the current status and prospects of QSAR modeling methods in the medical devices community.
Computer-aided drug design (CADD) is a widely used technology using computational tools and resources for the storage, management, analysis and modeling of compounds. It relies on digital repositories for study of designing compounds with physicochemical characteristics, predicting whether a given molecule will be combined with the target, and if so how strongly. Computer based methods can help us to search new hits in drug discovery, screen many irrelevant compounds at the same time and study the structure-activity relationship of drug molecules.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
Cheminformatics, concept by kk sahu sirKAUSHAL SAHU
INTRODUCTION
THE NEED FOR CHEMOINFORMATICS
CHEMOINFORMATICS AND DRUG DISCOVERY
HISTORICAL EVOLUTION
BASIC CONCEPTS
Chemistry Space
Molecular Descriptors
High-Throughput Screening
The Similar-Structure, Similar-Property Principle
Graph theory and Chemoinformatics
CHEMOINFORMATICS TASKS
MOLECULAR REPRESENTATIONS
Topological Representations
Geometrical Representations
TYPES OF MOLECULAR DESCRIPTORS
IN SILICO DE NOVO MOLECULAR DESIGN
FREE CHEMISTRY DATABASE
FUTURE
CONCLUSION
REFERENCE
How we Built a Large Scale Matched Pair Analysis Engine (MCPairs) using OpenE...Al Dossetter
MCPairs performed Matched Molecular Pair Analysis on large scale to build databases of exploitable knowledge which is accessible for Drug Discovery to accelerate research projects. The talk describes how we did this and some of the challenges.
ANALYSIS OF PROTEIN MICROARRAY DATA USING DATA MININGijbbjournal
Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and
tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data
mining research has led to the development of numerous efficient and scalable methods for mining
interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics
for successful mining of biological data. Microarrays constitute a new platform which allows the discovery
and characterization of proteins.
Future Directions in Chemical Engineering and BioengineeringIlya Klabukov
"Future Directions in Chemical Engineering and Bioengineering"
January 16-18, 2013
Austin, Texas
Chair: John G. Ekerdt, The University of Texas at Austin
Sponsored by Department of Defense,
Office of the Assistant Secretary of Defense for Research and Engineering
Chemical and biological engineers use math, physics, chemistry, and biology to develop chemical transformations and processes, creating useful products and materials that improve society. In recent years, the boundaries between chemical engineering and bioengineering have blurred as biology has become molecular science, more seamlessly connecting with the historic focus of chemical engineering on molecular interactions and transformations.
This disappearing boundary creates new opportunities for the next generation of engineered systems – hybrid systems that integrate the specificity of biology with chemical and material systems to enable novel applications in catalysis, biomaterials, electronic materials, and energy conversion materials.
Basic research for the U.S. Department of Defense covers a wide range of topics such as metamaterials and plasmonics, quantum information science, cognitive neuroscience, understanding human behavior, synthetic biology, and nanoscience and nanotechnology. Future Directions workshops such as this one identify opportunities
for continuing and future DOD investment. The intent is to create conditions for discovery and transformation, maximize the discovery potential, bring balance and coherence, and foster connections. Basic research stretches the limits of today’s technologies and discovers new phenomena and know-how that ultimately lead to future technologies and enable military and societal progress.
ChemSpider was developed with the intention of aggregating and indexing available sources of chemical structures and their associated information into a single searchable repository and making it available to everybody, at no charge. There are many tens of chemical structure databases such as literature data, chemical vendor catalogs, molecular properties, environmental data, toxicity data, analytical data etc. and no single way to search across them. Despite the diversity of databases available online their inherent quality, accuracy and completeness is lacking in many regards. ChemSpider was established to provide a platform whereby the chemistry community could contribute to cleaning up the data, improving the quality of data online and expanding the information available to include data such as reaction syntheses, analytical data and experimental properties. ChemSpider has now grown into a database of well over 20 million chemical substances integrated with over 300 disparate data sources, many of these directly supporting the Life Sciences. This presentation will provide an overview of our efforts to improve the quality of data online, to provide a foundation for the semantic web for chemistry and to provide access to a set online tools and services to support access to these data. I will also discuss how ChemSpider is being used to enhance Semantic Publishing in Chemistry at RSC.
This is a presentation I gave at the FDA on December 1st 2009 in Wahington DC as part of a symposium involving PubChem, ChemIDPLus, PillBox, DailyMed and other related systems. The focus was, as usual, on the quality of data online and how to clean up the information and with a specific focus on the quality of data on the FDA's DailyMed and our efforts to apply semantic markup to the DailyMed articles
This was a presentation I gave to an audience at Nature Publishing Group in New York on May 7th 2009. It's a long presentation and over an hour in length. Not much new here relative to other presentations...just a knitting together of many of the others on here.
There is an increasing availability of free and open access resources for scientists to use on the internet. Coupled with an increasing number of Open Source software programs we are in the middle of a revolution in data availability and tools to manipulate these data. ChemSpider is a free access website built with the intention of providing a structure centric community for chemists. As an aggregator of chemistry related information from many sources, at present over 21.5 million unique chemical entities from over 190 separate data sources, ChemSpider has taken on the task of both robotically and manually integrating and curating publicly available data sources. ChemSpider has also provided an environment for users to deposit, curate and annotate chemistry-related information. This has allowed the community to enhance ChemSpider by adding analytical data, associating synthetic pathways and publications and connecting to social networking resources. I will discuss how ChemSpider is fast becoming the premier curated platform and centralized hub for resourcing information about chemical entities and how the platform provides the foundation data for services allowing the analysis of analytical data and collaborative science.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
5. 1. Introduction
2. Molecular Representations
3. Chemical Data and Databases
4. Molecular Similarity
5. Chemical Reactions
6. Machine Learning and Other Predictive
Methods
7. Molecular Docking and Drug Discovery
6. • It encompasses the design, creation,
organisation, management, retrieval, analysis,
dissemination, visualization and use of chemical
information
• It is the mixing of information resources to
transform data into information and information
into knowledge, for the intended purpose of
making better decisions faster in the arena of
drug lead identification and optimization
7. • “the set of computer algorithms and tools
to store and analyse chemical data in the
context of drug discovery and design
projects”
• Chemoinformatics is the application of
informatics methods to solve chemical
problems
8. Books:
J. Gasteiger, T. E. and Engel, T. (Editors) (2003).
Chemoinformatics:ATextbook. Wiley.
A.R. Leach and V. J. Gillet (2005). An Introduction to
Chemoinformatics. Springer.
Journal:
Journal of Chemical Information and Modeling
Web:
http://cdb.ics.uci.edu
and many more………
9. The first, and still the core, journal for the subject, the Journal of Chemical
Documentation, started in 1961 (the name Changed to
the Journal of Chemical Information and computer Science in 1975)
The first book appeared in 1971 (Lynch, Harrison, Town and Ash,
Computer Handling of Chemical Structure Information)
The first international conference on the subject was held in 1973
at Noordwijkerhout and every three years since 1987
10. Chemoinformatics encompasses the design, creation, organisation, management,
retrieval, analysis, dissemination, visualization and use of chemical information
'Cheminformatics combines the scientific working fields of chemistry and
computer science for example in the area of chemical graph theory and
mining the chemical space. It is to be expected that the chemical space
contains at least 1062 molecules
11. Year Cheminformatics Chemoinformatics Ratio
2000 39 684 0.05
2001 8,010 2,910 2.75
2002 34,000 16,000 2.12
2203 58,143 32,872 1.77
2204 85,435 60,439 1.41
2005 6,58,298 2,72,096 2.41
2006 3,17,000+ 1,63,000+ 1.94
• Cheminformatics, molecular informatics, chemical
informatics, or even Chemo bioinformatics
12. 1) An enormous amount of data and maintenance of data
2)Can we gain enough knowledge from the known data to make
predictions for those cases where the required information is
not available?
3) Relationships between the structure of a compound and its
biological activity, or for the influence of reaction conditions
on chemical reactivity.
13. Advances in theoretical and computational chemistry now allow
chemists to model chemical compounds “in silico” with ever-
increasing accuracy.
Molecular properties now becoming accessible through
computation include molecular shape, electronic structure,
physical properties, chemical reactivity, protein folding, structures
of materials and surfaces, catalytic activity, and biochemical
activities.
14. integrates a comprehensive knowledge of chemistry with
an extensive understanding of information technology.
The intersection of chemistry and information
technology embraces an expanding territory;
computational modeling of individual molecules,
thermodynamic methods of estimating chemical
properties, methods of predicting biological activity of
hypothetical compounds, and organization and
classification of chemical information.
15. Schematic representationof a crowded cel . An
array of differentmoleculescan function
independentlyunderextremelycrowded
conditions, partly because of judicious
distributions of oppositelychargedpolargroups
onthe molecularsurfaces. H
owever, such
systems are insome ways extremely fragile.
Forexample, a mutationthat alters just one
amino acidinthe haemoglobin molecule can
stimulate massive aggregationandgive rise to
a fatalgenetic disease, sickle-cel anaemia.
Moregeneraly, manydisorders of oldage, most
famouslyAlzheimer’s disease, result fromthe
increasingly facile conversionof normal y
solubleproteinsintointractabledepositsthat
occur particularly as we get older M
any of these
aggregation processesinvolvethereversionof
the unique biological y active forms of
polypeptidechainsintoa genericand non-
functional ‘ chemical’ form
16. Additional computational challenges lie in indexing and
classifying the infinite population of chemical compounds that
could be synthesized or are already known.
Specific indexing and search problems include
how to find a compound that might block a specific biological
target;
how to predict the most efficient synthetic strategy for a desired
compound from available precursors;
how to employ results of bioactivity tests on a family of
molecules to design improved versions;
18. Currently combinatorial chemists are developing new methods of
synthesizing libraries of related compounds on an unprecedented
scale.
Such libraries can be used to produce huge arrays of materials for
investigation of biochemical, catalytic, or material properties.
Systems are required to design, catalog, and search these libraries,
assess test results in a meaningful way, and integrate new
information with existing chemical databases.
Investigations into information storage at the molecular level are
underway, bringing to full circle the link between chemistry and
information technology.
19. 22
Representations and Structure Searching
Substructure Searching
Similarity Searching, Clustering, and Diversity Analysis
Searching Databases
Computer-aided Structure Elucidation
3D Substructure Searching
QSAR and Docking
20. Structure and applications of chemoinformatics
Database design and programming
Representation and searching of chemical structures
Structure, substructure & similarity searching in 2D & 3D
Markush and reaction searching
Representation and searching of biological databases
chemoinformatics software
Data analysis techniques
Clustering;
Evolutionary algorithms;
Graph theory;
Neural networks;
Chemical information sources
Cheminformatics applications
Techniques used to design bioactive compounds
Molecular simulation and design
Drug discovery process; QSAR; Combi-chem; SBDD
Spectroscopy and crystallography in cheminformatics
21. • Small-molecule databases
–Databases of commercially-available compounds (e.g. ACD,
http://www.mdl.com/products/experiment/available_chem_dir/index.jsp)
– Proprietary chemical structure databases
– Literature databases
– Patent databases
– Small project-specific databases
• Protein databases
– Public, online databases (e.g. PDB, http://www.pdb.org)
– Proprietary and project-specific databases
22. Software Companies
Accelrys -Large chemoinformatics company
ACD/Labs - analytical informatics & predictions
BCI - 2D fingerprinting, clustering toolkits & software
Bioreason - HTS data analysis software
Cambridgesoft - 2D drawing tools & E-notebooks
CAS - produce Scifinder Scholar searching software
ChemAxon - Java based toolkits and software
Daylight- 2D representation & searching software
Leadscope - 2D structure and property tools
Lion Bioscience - produce LeadNavigator
MDL - Large chemoinformatics company
Openeye - Fast 3D docking, structure generation, toolkits
Quantum Pharmaceuticals - prediction, docking, screening
Sage Informatics - ChemTK 2D analysis software
Tripos-Large chemoinformatics company
23. Journal of Chemical Information and Computer Sciences
Journal of Computer-Aided Molecular Design
Journal of Molecular Graphics and Modelling
Journal of Medicinal Chemistry
NetSci (online journal)
Scientific Computing World
Bio-IT World
Drug Discovery Today
Newsletters, Mailing Lists & Other Hubs
Chemical Informatics Letters- Monthly newsletter
CHMINF-L (Indiana)- Email discussion list
Chemoinf Yahoo Group -Email discussion list
Chemistry Software Yahoo Group
Cheminformatics.org Lots of links and QSAR datasets
Reactive Reports Chemistry Web Magazine
25. Web-based drawing tools
JME (http://www.molinspiration.com/cgi-bin/properties) is a clean, simple Java drawing tool.
Draw your structure and click on the smiley face to show the SMILES.
Marvin Sketch is a Java applet that allows you to draw structures, and export them as
SMILES, MDL MOL files or others.
Web-based depiction tools
Daylight Depiction Tool (http://www.daylight.com/daycgi/depict) is a very simple to use tool
that allows you to enter a SMILES string and will then produce a 2D structure diagram from
it.
CACTVS GIF generator has a more complex interface, but allows many more options for
producing GIF picture files of SMILES or other format structures. The quality of the images
is superior to the daylight tool.
MDL Chime (http://www.mdlchime.com) is a browser-based plugin that can display both 2D
and interactive 3D structures in web pages.
27. Concord from Tripos, Inc. One of the first 3D structure generation
programs, and is still being refined and developed. It generates single,
minimal-energy structures from input 2D structures. The program can
input and output a variety of file formats.
http://www.tripos.com/sciTech/inSilicoDisc/chemInfo/concord.html
Corina from the Gasteiger group. It is similar to Concord.
http://www2.chemie.uni-erlangen.de/software/corina/free_struct.html
Omega from OpenEye is the latest release. It offers very fast generation
of multiple low-energy conformers.
http://www.eyesopen.com/products/applications/omega.html
28. MDL Chime is a web browser plug-in that allows 2D and 3D structures to be
viewed in web pages. It can be used to visualize both proteins and small
molecules, and includes some limited ability to create molecular surfaces. It
is excellent for communicating structures via the web and for use in writing
web-based chemoinformatics software. http://www.mdlchime.com
ArgusLab is a free molecular modeling program that has a fairly extensive
set of options for 3D visualization, calculation of surfaces and properties,
minimization, and molecular docking. http://www.arguslab.com.
31. Input
• Quantitative analysis of chemical data relied exclusively on
Multilinear regression analysis.
• Artificial neural networks
An artificial neural network (ANN) or commonly just neural network (NN)
is an interconnected group of artificial neurons that uses a mathematical
model or computational model for information processing based on a
connectionist approach to computation.
Hidden
Hidden
Output
32. 32
• A field of exercise for artificial intelligence techniques.
• The DENDRAL project, initiated in 1964 at Stanford University
Computer-Assisted Synthesis Design (CASD)
In 1969 Corey and Wipke worked for the development of a
synthesis design system.
1. Substructure searching
2. Similarity searching
33. 33
1. Chemical Information
Storage and retrieval of chemical structures and associated
data to manage the flood of data
Dissemination of data on the internet
Cross-linking of data to information
2. All fields of chemistry
• Prediction of the physical, chemical, or biological properties
of compounds
34. • identification of new lead structures
• optimization of lead structures
• establishment of quantitative structure-activity relationships
• comparison of chemical libraries
• definition and analysis of structural diversity
•planning of chemical libraries
37
35. Contd……
• analysis of high-throughput data
• docking of a ligand into a receptor
• prediction of the metabolism of xenobiotics
• analysis of biochemical pathways
35
4. Organic Chemistry
• Prediction of the course and products of organic reactions
• Design of organic synthesis
36. • Analysis of data from analytical chemistry to make predictions on
the quality, origin, and age of the investigated objects
• Elucidation of the structure of a compound based on spectroscopic
data
36
Teaching Chemoinformatics
Chemists have to become more efficient in planning their
experiments, have to extract more knowledge from their
data
39. University of Barcelona, Spain
University of Erlangen-Nürnberg, Germany
Bioinformatics Institute Of India , Chandigarh
Georgia Institute of Technology
University of Sheffield (Willett) - MSc/PhD programs
University of Erlangen (Gasteiger)
UCSF (Kuntz)
University of Texas (Pearlman)
Yale (Jorgensen)
University of Michigan (Crippen)
Indiana University (Wiggins) - MSc program
Cambridge Unilever (Glen, Goodman, Murray-Rust)
Scripps - Molecular Graphics lab
39
40. DRUG DESIGN ENVIRONMENTAL PROTECTION
Maximum activity Prediction of toxicity
Minimize toxicity
•Single therapeutic target
•Drug like chemical
•Some toxicity anticipated
•Multiple unknown targets
•Diverse Structures
•Human and ecosystems